Publishing sensor observations into Geospatial Information Infrastructures: A use case in fire danger assessment

To improve environmental monitoring, the availability of large coverage, interoperable spatio-temporal data is crucial for its integration into environmental models, for example, to compute fire danger models. In order to produce up-to-date and accurate results, these models require data with high temporal and spatial resolution. Thus, it is promising to consider the increasing number of in-situ sensors providing observations of our environment in real-time. Today, interoperable access to such spatio-temporal data is achieved by Geospatial Information Infrastructures (GIIs). From a technical point of view, GIIs provide these data through standards-based Web service interfaces. While those Web service interfaces already enable the interoperable discovery and retrieval of sensor observations, the functionality to publish sensor observations is still an arduous task. Hence, in this paper, we present an approach to improve the registration of sensors and the publication of their observations via standards-based Web service interfaces. We evaluate our approach by extending a standards-based GII and by applying the developed approach as a proof of concept to integrate in-situ weather observations into the European Forest Fire Information System for assessing fire danger in Spain.     

面向水环境管理的空间数据仓库构建

从水环境管理的实际情况出发,提出了面向水环境管理的空间数据仓库构建策略,并对开发的管理系统进行了介绍,最后指出后继研究工作的重点。     

空间数据存储机制研究

在地理信息系统中，空间数据的存储至关重要，对空间数据的几种存储模型进行了分析探讨，指出全关系型的扩展数据模型或面向对象的关系数据模型是较佳的存储方式，关系对象数据库在GIS中将发挥更大的作用。     

空间数据插件技术研究

利用数据库技术来管理海量的空间数据,有两种方法:一种是在数据库和用户应用系统间建立中间层,称为空间数据引擎SDE,利用SDE作为中间接口使数据库操作系统能存储和管理空间数据;另一种是不需要中间接口而直接对数据库操作系统自身的功能进行扩展来存储管理空间数据,典型的代表就是Oracle公司提供的空间数据插件(Spa-tial)。由此看SDE和OracleSpatial好像是互斥的,取其一即可;实际上二者在处理空间数据的角度上定位不同,并不是互斥的,而是可以相互结合的,用户可以根据自己的具体应用来决定是选其一还是将二者结合。     

基于超集式GML数据结构的异构空间数据文件互转模型

为摆脱异构空间数据不易实现共享与互操作的困扰,设计并实现了异构空间数据文件互转模型系统,此系统构建在COM技术之上,其结构框架由三级子系统组成。首创性地提出了“超集式GML数据结构”这一概念,借助于这一数据结构,烦琐沉重的空间数据互转工作变得不再那么困难。     

空间数据挖掘技术方法及应用

着重阐述了通用的空间数据挖掘体系结构,空间数据的关联特性,几种主要的空间数据挖掘方法。最后对一实例进行了应用分析。     

Service-oriented applications for environmental models: Reusable geospatial services

Environmental modelling often requires a long iterative process of sourcing, reformatting, analyzing, and introducing various types of data into the model. Much of the data to be analyzed are geospatial data—digital terrain models (DTM), river basin boundaries, snow cover from satellite imagery, etc.—and so the modelling workflow typically involves the use of multiple desktop GIS and remote sensing software packages, with limited compatibility among them. Recent advances in service-oriented architectures (SOA) are allowing users to migrate from dedicated desktop solutions to on-line, loosely coupled, and standards-based services which accept source data, process them, and pass results as basic parameters to other intermediate services and/or then to the main model, which also may be made available on-line. This contribution presents a service-oriented application that addresses the issues of data accessibility and service interoperability for environmental models. Key model capabilities are implemented as geospatial services, which are combined to form complex services, and may be reused in other similar contexts. This work was carried out under the auspices of the AWARE project funded by the European programme Global Monitoring for Environment and Security (GMES). We show results of the service-oriented application applied to alpine runoff models, including the use of geospatial services facilitating discovery, access, processing and visualization of geospatial data in a distributed manner.     

SDSS中空间数据挖掘部件的设计与实现

空间数据挖掘就是从空间数据库中抽取隐含的知识、空间关系或空间数据库中存储的其他隐含模式的过程。空间数据挖掘在地理信息系统、空间遥感、测绘、资源和环境管理等很多领域都有广泛的应用前景。论文对空间数据挖掘的一些主要技术进行研究和探讨,介绍了基于这些技术设计实现的一个空间数据挖掘部件。     

多源空间数据的共享与集成模式研究

在研究现有多格式空间数据共享与集成模式的基础上,借鉴通用数据访问(UniversalDataAccess,UDA)技术为传统多源数据提供通用数据访问的思路,作者提出了基于通用空间数据引擎(UniversalSpatialDataEngine,USDE)的空间数据共享与集成模式。通用空间数据引擎由数据引擎管理器及相应的一系列数据引擎构成,其中数据引擎管理器负责数据引擎的智能调用,并为客户GIS软件提供统一的数据访问接口;各数据引擎负责对具体数据访问接口的实现。利用COM技术,作者设计出了基于目前几种流行的GIS数据格式的USDE,并成功地实现了对几种不同格式空间数据的集成应用。     

基于空间数据结构的祁连山青海云杉林水量平衡

空间数据结构的建立包括系统功能与数据的关系、数据的分类和编码、数据的输入和编辑等技术。基于空间数据库技术研究祁连山青海云杉林的降水、截留、干流、蒸发散等分配过程及变化规律, 结果表明: 全年降水为433. 5 mm; 透流量287. 37 mm; 干流0. 294 mm; 截留量145. 83 mm。分别占降水量的66. 29% 、0. 068% 、33. 64% 。径流量为239. 98 mm, 占全年降水的55. 36%, 蒸发散量为156. 54 mm 占降水量的36. 11%, 土壤储水量增量23. 63 mm, 枯落层持水量7. 92 mm, 分别占降水的5. 45% 、1. 83%。     

基于三维数据场可视化方法的虚拟MEMS加工工艺的研究

虚拟加工工艺是利用计算机产生三维图形的动态生长来模拟实际的加工过程,而如何生成真实自然的三维图形就成了关键所在。本文提出了一种基于三维数据场可视化来实现虚拟加工的方法。该方法类似于医学数据三维重建, 由一序列的分层切片的二维图像重构三维形体。首先根据给出的虚拟加工工艺数据对原始二维版图进行相应的数学形态学变换生成一系列所需的二维切片图像,然后利用这些二维切片图像对体数据场进行初始化,最后采用Marching Cubes方法从体数据场中构造出等值面。本文主要阐述了如何根据工艺参数对原始版图进行数学形态学变换以及如何构造体数据场进行等值面的绘制。     

Optimization tools for environmental water decisions: A review of strengths,weaknesses, and opportunities to improve adoption

Public investment in river restoration through environmental watering has increased substantially in recent years. To sustain public support for such investment, management of environmental water must achieve the best possible outcomes in a transparent and defensible manner. The current management of environmental water relies on the ability of managers to estimate the impacts of their decisions under complex scenarios, often with multiple interdependent decisions that span over different spatial and temporal scales. Optimization modeling has been widely used in other forms of conservation management and an increasing body of literature documents the development of optimization models that could be used to improve environmental water decisions. This paper reviews this disparate research, showing that there are a range of different questions addressed using this modeling approach and that the representation of environmental outcomes varies. Future work must focus on improved adoption through engagement with end users and stakeholders during model development.     

Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique

Spatial Decision Support Systems (SDSSs) often include models that can be used to assess the impact of possible decisions. These models usually simulate complex spatio-temporal phenomena, with input variables and parameters that are often hard to measure. The resulting model uncertainty is, however, rarely communicated to the user, so that current SDSSs yield clear, but therefore sometimes deceptively precise outputs. Inclusion of uncertainty in SDSSs requires modeling methods to calculate uncertainty and tools to visualize indicators of uncertainty that can be understood by its users, having mostly limited knowledge of spatial statistics. This research makes an important step towards a solution of this issue. It illustrates the construction of the PCRaster Land Use Change model (PLUC) that integrates simulation, uncertainty analysis and visualization. It uses the PCRaster Python framework, which comprises both a spatio-temporal modeling framework and a Monte Carlo analysis framework that together produce stochastic maps, which can be visualized with the Aguila software, included in the PCRaster Python distribution package. This is illustrated by a case study for Mozambique in which it is evaluated where bioenergy crops can be cultivated without endangering nature areas and food production now and in the near future, when population and food intake per capita will increase and thus arable land and pasture areas are likely to expand. It is shown how the uncertainty of the input variables and model parameters effects the model outcomes. Evaluation of spatio-temporal uncertainty patterns has provided new insights in the modeled land use system about, e.g., the shape of concentric rings around cities. In addition, the visualization modes give uncertainty information in an comprehensible way for users without specialist knowledge of statistics, for example by means of confidence intervals for potential bioenergy crop yields. The coupling of spatio-temporal uncertainty analysis to the simulation model is considered a major step forward in the exposure of uncertainty in SDSSs.     

A continuous variable Bayesian networks model for water quality modeling: A case study of setting nitrogen criterion for small rivers and streams in Ohio,USA

We present a continuous variable Bayesian networks modeling framework that integrates the graphical representation of a Bayesian networks model with empirical model-developing approach. Our model retains the Bayesian networks model's graphical representation of hypothesized causal connections among important variables and employs conventional statistical modeling approaches for establishing functional relationships among these variables. The modeling framework avoids discretizing continuous variables and the resulting models can be updated over time when new data are available or updated using local data to develop a site-specific model. We illustrate the modeling approach using a data for establishing nutrient criteria in streams and rivers in Ohio, U.S.A.